# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/ichimoku.py from freqtrade.strategy.interface import IStrategy from pandas import DataFrame from technical.indicators import Github_remiotore_freqtrade__ichimoku__20260111_210550 import talib.abstract as ta import freqtrade.vendor.qtpylib.indicators as qtpylib import numpy # noqa class Github_remiotore_freqtrade__ichimoku__20260111_210550(IStrategy): """ This is a sample strategy to inspire you. More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md You can: :return: a Dataframe with all mandatory indicators for the strategies - Rename the class name (Do not forget to update class_name) - Add any methods you want to build your strategy - Add any lib you need to build your strategy You must keep: - the lib in the section "Do not remove these libs" - the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend, populate_sell_trend, hyperopt_space, buy_strategy_generator """ INTERFACE_VERSION = 2 minimal_roi = { "0": 1, } stoploss = -0.05 trailing_stop = False trailing_stop_positive = -0.1 trailing_stop_positive_offset = 0.05 # Disabled / not configured trailing_only_offset_is_reached = True ticker_interval = '1m' process_only_new_candles = False use_sell_signal = False sell_profit_only = False ignore_roi_if_buy_signal = False order_types = { 'buy': 'limit', 'sell': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False } order_time_in_force = { 'buy': 'gtc', 'sell': 'gtc' } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. These pair/interval combinations are non-tradeable, unless they are part of the whitelist as well. For more information, please consult the documentation :return: List of tuples in the format (pair, interval) Sample: return [("ETH/USDT", "5m"), ("BTC/USDT", "15m"), ] """ return [] def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Adds several different TA indicators to the given DataFrame Performance Note: For the best performance be frugal on the number of indicators you are using. Let uncomment only the indicator you are using in your strategies or your hyperopt configuration, otherwise you will waste your memory and CPU usage. :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe() :param metadata: Additional information, like the currently traded pair :return: a Dataframe with all mandatory indicators for the strategies """ ichi=Github_remiotore_freqtrade__ichimoku__20260111_210550(dataframe) dataframe['tenkan']=ichi['tenkan_sen'] dataframe['kijun']=ichi['kijun_sen'] dataframe['senkou_a']=ichi['senkou_span_a'] dataframe['senkou_b']=ichi['senkou_span_b'] dataframe['cloud_green']=ichi['cloud_green'] dataframe['cloud_red']=ichi['cloud_red'] dataframe['adx'] = ta.ADX(dataframe) """ dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) dataframe['cci'] = ta.CCI(dataframe) macd = ta.MACD(dataframe) dataframe['macd'] = macd['macd'] dataframe['macdsignal'] = macd['macdsignal'] dataframe['macdhist'] = macd['macdhist'] dataframe['mfi'] = ta.MFI(dataframe) dataframe['minus_dm'] = ta.MINUS_DM(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['plus_dm'] = ta.PLUS_DM(dataframe) dataframe['plus_di'] = ta.PLUS_DI(dataframe) dataframe['minus_di'] = ta.MINUS_DI(dataframe) dataframe['roc'] = ta.ROC(dataframe) dataframe['rsi'] = ta.RSI(dataframe) rsi = 0.1 * (dataframe['rsi'] - 50) dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1) dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) stoch = ta.STOCH(dataframe) dataframe['slowd'] = stoch['slowd'] dataframe['slowk'] = stoch['slowk'] stoch_fast = ta.STOCHF(dataframe) dataframe['fastd'] = stoch_fast['fastd'] dataframe['fastk'] = stoch_fast['fastk'] stoch_rsi = ta.STOCHRSI(dataframe) dataframe['fastd_rsi'] = stoch_rsi['fastd'] dataframe['fastk_rsi'] = stoch_rsi['fastk'] """ bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] """ dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) dataframe['sar'] = ta.SAR(dataframe) dataframe['sma'] = ta.SMA(dataframe, timeperiod=40) """ dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) hilbert = ta.HT_SINE(dataframe) dataframe['htsine'] = hilbert['sine'] dataframe['htleadsine'] = hilbert['leadsine'] """ dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] """ """ dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) """ """ dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] """ """ heikinashi = qtpylib.heikinashi(dataframe) dataframe['ha_open'] = heikinashi['open'] dataframe['ha_close'] = heikinashi['close'] dataframe['ha_high'] = heikinashi['high'] dataframe['ha_low'] = heikinashi['low'] """ """ if self.dp: if self.dp.runmode in ('live', 'dry_run'): ob = self.dp.orderbook(metadata['pair'], 1) dataframe['best_bid'] = ob['bids'][0][0] dataframe['best_ask'] = ob['asks'][0][0] """ return dataframe def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the buy signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( (dataframe['tenkan'].shift(1) < dataframe['kijun'].shift(1)) & (dataframe['tenkan'] > dataframe['kijun']) & (dataframe['cloud_red']==True) ), 'buy'] = 1 return dataframe def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ Based on TA indicators, populates the sell signal for the given dataframe :param dataframe: DataFrame populated with indicators :param metadata: Additional information, like the currently traded pair :return: DataFrame with buy column """ dataframe.loc[ ( ), 'sell'] = 1 return dataframe